Overview

Dataset statistics

Number of variables9
Number of observations984
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory76.9 KiB
Average record size in memory80.0 B

Variable types

Numeric9

Alerts

cases_new is highly overall correlated with cases_import and 7 other fieldsHigh correlation
cases_import is highly overall correlated with cases_new and 5 other fieldsHigh correlation
cases_recovered is highly overall correlated with cases_new and 7 other fieldsHigh correlation
cases_active is highly overall correlated with cases_new and 7 other fieldsHigh correlation
cases_cluster is highly overall correlated with cases_new and 7 other fieldsHigh correlation
deaths_new is highly overall correlated with cases_new and 7 other fieldsHigh correlation
deaths_bid is highly overall correlated with cases_new and 6 other fieldsHigh correlation
deaths_new_dod is highly overall correlated with cases_new and 7 other fieldsHigh correlation
deaths_bid_dod is highly overall correlated with cases_new and 6 other fieldsHigh correlation
cases_import has 609 (61.9%) zerosZeros
cases_recovered has 15 (1.5%) zerosZeros
cases_cluster has 504 (51.2%) zerosZeros
deaths_new has 379 (38.5%) zerosZeros
deaths_bid has 646 (65.7%) zerosZeros
deaths_new_dod has 343 (34.9%) zerosZeros
deaths_bid_dod has 624 (63.4%) zerosZeros

Reproduction

Analysis started2023-11-11 23:19:14.958905
Analysis finished2023-11-11 23:19:33.503481
Duration18.54 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

cases_new
Real number (ℝ)

HIGH CORRELATION 

Distinct774
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1507.4278
Minimum11
Maximum11692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2023-11-11T23:19:33.624801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile37
Q1176.5
median731
Q31559.25
95-th percentile6852.75
Maximum11692
Range11681
Interquartile range (IQR)1382.75

Descriptive statistics

Standard deviation2131.4369
Coefficient of variation (CV)1.4139561
Kurtosis4.1355777
Mean1507.4278
Median Absolute Deviation (MAD)613.5
Skewness2.1601629
Sum1483309
Variance4543023.1
MonotonicityNot monotonic
2023-11-11T23:19:33.812742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 7
 
0.7%
52 6
 
0.6%
48 5
 
0.5%
58 5
 
0.5%
76 5
 
0.5%
51 5
 
0.5%
53 5
 
0.5%
26 4
 
0.4%
46 4
 
0.4%
44 4
 
0.4%
Other values (764) 934
94.9%
ValueCountFrequency (%)
11 1
 
0.1%
13 1
 
0.1%
14 2
0.2%
15 1
 
0.1%
16 1
 
0.1%
18 1
 
0.1%
19 3
0.3%
21 1
 
0.1%
22 2
0.2%
23 1
 
0.1%
ValueCountFrequency (%)
11692 1
0.1%
10842 1
0.1%
10790 1
0.1%
10735 1
0.1%
10623 1
0.1%
10240 1
0.1%
9674 1
0.1%
9651 1
0.1%
9273 1
0.1%
9120 1
0.1%

cases_import
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0538618
Minimum0
Maximum230
Zeros609
Zeros (%)61.9%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2023-11-11T23:19:33.999139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile57.85
Maximum230
Range230
Interquartile range (IQR)2

Descriptive statistics

Standard deviation28.21452
Coefficient of variation (CV)3.1162968
Kurtosis22.436364
Mean9.0538618
Median Absolute Deviation (MAD)0
Skewness4.4898933
Sum8909
Variance796.05915
MonotonicityNot monotonic
2023-11-11T23:19:34.184278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 609
61.9%
1 70
 
7.1%
2 61
 
6.2%
3 33
 
3.4%
5 20
 
2.0%
4 20
 
2.0%
7 13
 
1.3%
6 9
 
0.9%
9 8
 
0.8%
14 6
 
0.6%
Other values (77) 135
 
13.7%
ValueCountFrequency (%)
0 609
61.9%
1 70
 
7.1%
2 61
 
6.2%
3 33
 
3.4%
4 20
 
2.0%
5 20
 
2.0%
6 9
 
0.9%
7 13
 
1.3%
8 5
 
0.5%
9 8
 
0.8%
ValueCountFrequency (%)
230 1
0.1%
201 1
0.1%
200 1
0.1%
192 2
0.2%
190 1
0.1%
189 1
0.1%
188 1
0.1%
177 1
0.1%
168 1
0.1%
157 1
0.1%

cases_recovered
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct765
Distinct (%)77.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1506.8608
Minimum0
Maximum12379
Zeros15
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2023-11-11T23:19:34.366060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34.15
Q1204
median751
Q31580
95-th percentile6881.7
Maximum12379
Range12379
Interquartile range (IQR)1376

Descriptive statistics

Standard deviation2139.1166
Coefficient of variation (CV)1.4195848
Kurtosis4.6388241
Mean1506.8608
Median Absolute Deviation (MAD)625
Skewness2.2445813
Sum1482751
Variance4575820
MonotonicityNot monotonic
2023-11-11T23:19:34.575711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
1.5%
51 7
 
0.7%
37 6
 
0.6%
56 5
 
0.5%
48 5
 
0.5%
52 5
 
0.5%
60 5
 
0.5%
35 5
 
0.5%
76 4
 
0.4%
50 4
 
0.4%
Other values (755) 923
93.8%
ValueCountFrequency (%)
0 15
1.5%
11 1
 
0.1%
13 1
 
0.1%
14 2
 
0.2%
16 1
 
0.1%
18 1
 
0.1%
19 2
 
0.2%
20 1
 
0.1%
21 1
 
0.1%
22 2
 
0.2%
ValueCountFrequency (%)
12379 1
0.1%
11641 1
0.1%
10801 1
0.1%
10736 1
0.1%
10445 1
0.1%
10198 1
0.1%
9712 1
0.1%
9642 1
0.1%
9602 1
0.1%
9236 1
0.1%

cases_active
Real number (ℝ)

HIGH CORRELATION 

Distinct948
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17536.999
Minimum-390
Maximum103574
Zeros0
Zeros (%)0.0%
Negative48
Negative (%)4.9%
Memory size15.4 KiB
2023-11-11T23:19:34.904990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-390
5-th percentile91.35
Q11761.25
median7584.5
Q317480
95-th percentile83527.2
Maximum103574
Range103964
Interquartile range (IQR)15718.75

Descriptive statistics

Standard deviation25051.718
Coefficient of variation (CV)1.4285065
Kurtosis2.8030598
Mean17536.999
Median Absolute Deviation (MAD)6304.5
Skewness1.9870718
Sum17256407
Variance6.2758856 × 108
MonotonicityNot monotonic
2023-11-11T23:19:35.147680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1129 3
 
0.3%
1082 3
 
0.3%
10086 2
 
0.2%
1406 2
 
0.2%
5305 2
 
0.2%
1066 2
 
0.2%
-359 2
 
0.2%
5207 2
 
0.2%
5046 2
 
0.2%
1000 2
 
0.2%
Other values (938) 962
97.8%
ValueCountFrequency (%)
-390 1
0.1%
-369 1
0.1%
-368 1
0.1%
-366 1
0.1%
-362 1
0.1%
-359 2
0.2%
-328 1
0.1%
-316 1
0.1%
-296 1
0.1%
-288 1
0.1%
ValueCountFrequency (%)
103574 1
0.1%
102342 1
0.1%
100986 1
0.1%
98611 1
0.1%
97080 1
0.1%
96932 1
0.1%
96004 1
0.1%
95768 1
0.1%
95631 1
0.1%
95170 1
0.1%

cases_cluster
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct252
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.197154
Minimum0
Maximum1035
Zeros504
Zeros (%)51.2%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2023-11-11T23:19:35.512275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q370
95-th percentile372.7
Maximum1035
Range1035
Interquartile range (IQR)70

Descriptive statistics

Standard deviation135.84168
Coefficient of variation (CV)2.0215392
Kurtosis10.212042
Mean67.197154
Median Absolute Deviation (MAD)0
Skewness2.9469062
Sum66122
Variance18452.962
MonotonicityNot monotonic
2023-11-11T23:19:35.874136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 504
51.2%
1 16
 
1.6%
15 11
 
1.1%
11 9
 
0.9%
17 7
 
0.7%
20 7
 
0.7%
8 7
 
0.7%
13 7
 
0.7%
18 6
 
0.6%
3 6
 
0.6%
Other values (242) 404
41.1%
ValueCountFrequency (%)
0 504
51.2%
1 16
 
1.6%
2 4
 
0.4%
3 6
 
0.6%
4 3
 
0.3%
5 5
 
0.5%
6 3
 
0.3%
7 4
 
0.4%
8 7
 
0.7%
9 6
 
0.6%
ValueCountFrequency (%)
1035 1
0.1%
931 1
0.1%
915 1
0.1%
745 1
0.1%
696 1
0.1%
687 1
0.1%
674 1
0.1%
645 1
0.1%
640 1
0.1%
610 1
0.1%

deaths_new
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct94
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.942073
Minimum0
Maximum359
Zeros379
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2023-11-11T23:19:36.207662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35.25
95-th percentile71.55
Maximum359
Range359
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation29.332168
Coefficient of variation (CV)2.6806774
Kurtosis34.573069
Mean10.942073
Median Absolute Deviation (MAD)1
Skewness4.9667454
Sum10767
Variance860.37609
MonotonicityNot monotonic
2023-11-11T23:19:36.561943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 379
38.5%
1 151
 
15.3%
2 82
 
8.3%
3 59
 
6.0%
4 36
 
3.7%
5 31
 
3.2%
6 21
 
2.1%
8 15
 
1.5%
7 14
 
1.4%
10 12
 
1.2%
Other values (84) 184
18.7%
ValueCountFrequency (%)
0 379
38.5%
1 151
 
15.3%
2 82
 
8.3%
3 59
 
6.0%
4 36
 
3.7%
5 31
 
3.2%
6 21
 
2.1%
7 14
 
1.4%
8 15
 
1.5%
9 10
 
1.0%
ValueCountFrequency (%)
359 1
0.1%
253 1
0.1%
222 1
0.1%
188 1
0.1%
178 1
0.1%
171 1
0.1%
166 1
0.1%
149 1
0.1%
148 2
0.2%
147 1
0.1%

deaths_bid
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.398374
Minimum0
Maximum84
Zeros646
Zeros (%)65.7%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2023-11-11T23:19:36.920341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile11.85
Maximum84
Range84
Interquartile range (IQR)1

Descriptive statistics

Standard deviation8.2062575
Coefficient of variation (CV)3.4215921
Kurtosis38.130718
Mean2.398374
Median Absolute Deviation (MAD)0
Skewness5.759238
Sum2360
Variance67.342663
MonotonicityNot monotonic
2023-11-11T23:19:37.149875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 646
65.7%
1 119
 
12.1%
2 73
 
7.4%
3 27
 
2.7%
4 20
 
2.0%
5 15
 
1.5%
7 11
 
1.1%
6 10
 
1.0%
8 8
 
0.8%
13 4
 
0.4%
Other values (34) 51
 
5.2%
ValueCountFrequency (%)
0 646
65.7%
1 119
 
12.1%
2 73
 
7.4%
3 27
 
2.7%
4 20
 
2.0%
5 15
 
1.5%
6 10
 
1.0%
7 11
 
1.1%
8 8
 
0.8%
9 2
 
0.2%
ValueCountFrequency (%)
84 1
0.1%
71 1
0.1%
69 1
0.1%
67 1
0.1%
64 1
0.1%
63 1
0.1%
58 1
0.1%
55 1
0.1%
50 2
0.2%
48 1
0.1%

deaths_new_dod
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.889228
Minimum0
Maximum191
Zeros343
Zeros (%)34.9%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2023-11-11T23:19:37.408942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile63.85
Maximum191
Range191
Interquartile range (IQR)6

Descriptive statistics

Standard deviation29.091796
Coefficient of variation (CV)2.6716124
Kurtosis16.11494
Mean10.889228
Median Absolute Deviation (MAD)1
Skewness3.9713747
Sum10715
Variance846.33258
MonotonicityNot monotonic
2023-11-11T23:19:37.716835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 343
34.9%
1 177
18.0%
2 101
 
10.3%
3 52
 
5.3%
4 38
 
3.9%
7 22
 
2.2%
5 22
 
2.2%
6 21
 
2.1%
8 17
 
1.7%
10 11
 
1.1%
Other values (85) 180
18.3%
ValueCountFrequency (%)
0 343
34.9%
1 177
18.0%
2 101
 
10.3%
3 52
 
5.3%
4 38
 
3.9%
5 22
 
2.2%
6 21
 
2.1%
7 22
 
2.2%
8 17
 
1.7%
9 10
 
1.0%
ValueCountFrequency (%)
191 1
 
0.1%
184 1
 
0.1%
170 1
 
0.1%
167 1
 
0.1%
165 2
0.2%
164 1
 
0.1%
163 2
0.2%
162 1
 
0.1%
159 1
 
0.1%
158 3
0.3%

deaths_bid_dod
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3902439
Minimum0
Maximum64
Zeros624
Zeros (%)63.4%
Negative0
Negative (%)0.0%
Memory size15.4 KiB
2023-11-11T23:19:38.112025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile14
Maximum64
Range64
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.3261573
Coefficient of variation (CV)3.065025
Kurtosis25.39108
Mean2.3902439
Median Absolute Deviation (MAD)0
Skewness4.766757
Sum2352
Variance53.67258
MonotonicityNot monotonic
2023-11-11T23:19:38.282946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 624
63.4%
1 143
 
14.5%
2 58
 
5.9%
3 39
 
4.0%
4 27
 
2.7%
5 10
 
1.0%
7 9
 
0.9%
10 7
 
0.7%
14 6
 
0.6%
8 5
 
0.5%
Other values (32) 56
 
5.7%
ValueCountFrequency (%)
0 624
63.4%
1 143
 
14.5%
2 58
 
5.9%
3 39
 
4.0%
4 27
 
2.7%
5 10
 
1.0%
6 4
 
0.4%
7 9
 
0.9%
8 5
 
0.5%
9 2
 
0.2%
ValueCountFrequency (%)
64 1
0.1%
57 1
0.1%
55 1
0.1%
54 1
0.1%
53 1
0.1%
50 1
0.1%
47 1
0.1%
46 1
0.1%
45 1
0.1%
41 1
0.1%

Interactions

2023-11-11T23:19:31.423838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:15.120309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:18.590400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:20.101570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:21.892603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:24.751018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:26.285214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:27.872327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:29.337153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:31.616586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:15.284640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:18.755271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:20.277351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:22.190047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:24.916013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:26.470739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:28.039862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:29.509946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:31.780878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:17.366691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:18.914103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:20.434421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:22.460363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:25.073737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:26.639144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:28.196891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:29.670691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:31.957146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:17.548453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:19.097680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:20.606015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:22.684576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:25.271283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:26.811296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:28.367650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:29.850962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:32.236581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:17.711322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:19.262518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:20.764656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:23.090441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:25.438091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:26.971382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:28.539846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:30.021360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:32.405210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:17.876297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:19.416534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:20.936000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:23.280106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:25.597748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:27.142955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:28.691406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:30.194287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:32.589772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:18.057034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:19.594119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:21.121105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:23.889326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:25.769441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:27.341064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:28.858998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:30.392209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:32.748557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:18.230756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:19.749046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:21.282880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:24.186484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:25.922966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:27.512957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:29.000387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:30.682253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:32.926898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:18.418121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:19.927122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:21.589054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:24.552387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:26.105880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:27.697741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:29.178295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-11T23:19:30.870896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-11-11T23:19:38.436055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
cases_newcases_importcases_recoveredcases_activecases_clusterdeaths_newdeaths_biddeaths_new_doddeaths_bid_dod
cases_new1.0000.6200.9300.9440.6940.7610.6730.8240.698
cases_import0.6201.0000.5990.6540.6300.5530.4860.5860.494
cases_recovered0.9300.5991.0000.9330.6570.7500.6720.7940.674
cases_active0.9440.6540.9331.0000.7080.7770.6980.8300.714
cases_cluster0.6940.6300.6570.7081.0000.7160.5980.7440.646
deaths_new0.7610.5530.7500.7770.7161.0000.7950.7810.686
deaths_bid0.6730.4860.6720.6980.5980.7951.0000.6830.666
deaths_new_dod0.8240.5860.7940.8300.7440.7810.6831.0000.797
deaths_bid_dod0.6980.4940.6740.7140.6460.6860.6660.7971.000

Missing values

2023-11-11T23:19:33.156583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-11T23:19:33.400406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

cases_newcases_importcases_recoveredcases_activecases_clusterdeaths_newdeaths_biddeaths_new_doddeaths_bid_dod
date
2021-02-2458111322105402252120
2021-02-256662160495931959110
2021-02-266332114990731544110
2021-02-278620113288012552010
2021-02-28914194387694433030
2021-03-014531100682161450020
2021-03-02672190579801563040
2021-03-03640269379261711040
2021-03-04630271778361383010
2021-03-057942111075152605110
cases_newcases_importcases_recoveredcases_activecases_clusterdeaths_newdeaths_biddeaths_new_doddeaths_bid_dod
date
2023-10-2677053112900000
2023-10-2776047115800000
2023-10-2878046119000000
2023-10-2944026120800000
2023-10-3053055120600000
2023-10-3167080119300000
2023-11-0188074120700000
2023-11-0295077122500000
2023-11-0392076124100000
2023-11-04890256107400000